Inventory allocation for advertising with changeable supply landscape

ABSTRACT

An advertisement impression distribution system is programmed to generate an allocation plan for serving a number of advertisement impressions changeable as a result of one or more events, the allocation plan to allocate a first portion of advertisement impressions to satisfy guaranteed demand and a second portion of advertisement impressions to satisfy non-guaranteed demand. The system includes an optimizer programmed to establish a relationship between the first portion of advertisement impressions and the second portion of advertisement impressions, the relationship defining a range of possible proportions of allocation of the first portion of advertisement impressions and the second portion of advertisement impressions; and to impose at least one objective on the relationship including moderating an increase in the number of advertisement impressions available for allocation to the first and second portions, to minimize a cost associated with reducing a quality of the advertisement impressions as their volume increases. The system outputs the allocation plan to an ad serving module to control serving of the advertisement impressions according to the range of possible proportions of allocation between the first and the second portions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to, but does not claim the priority benefitof, commonly-assigned U.S. patent application Ser. No. 12/609,396, filedOct. 30, 2008, now U.S. Pat. No. 8,311,884, issued Nov. 13, 2012, whichis a continuation-in-part of U.S. patent application Ser. No.12/241,657, filed Sep. 30, 2008, the entirety of both of which areincorporated by reference herein.

TECHNICAL FIELD

The present description relates generally to systems and methods forproviding a display advertising optimization model including balancingobjectives such as: minimizing a supply cost of ad impressions,maximizing guaranteed demand representativeness, maximizingnon-guaranteed revenue, and minimizing under-delivery penalties.

BACKGROUND

A market exists for the distribution of advertising and otherinformation over data communications and entertainment networks. Anon-limiting example is insertion of advertising copy supplied byadvertisers for appearance on web pages having content offered by mediadistributors such as news and information services, internet serviceproviders, and suppliers of products related to the products or servicesof the advertiser.

The value of an opportunity to present an ad (i.e., to exploit an “adimpression”) is different for different advertisers and different webpage or entertainment genres, because the content of the media deliveredby a particular media outlet draws users of a certain type that maycorrelate more or less strongly with a population of potential customersthat an advertiser seeks to reach. Variation in the value of ads, theability to discriminate among ad recipients as a function of thevariable content of the web pages they access, and the ability to shiftselectively to route appropriate ad content to a selected user when aweb page is rendered all make on-line network communications a usefuland efficient environment for advertising, and especially for targetedadvertising.

The network could be the Worldwide Web, and the advertising copy couldcomprise banner ads, graphics in fields of specific size and placement,overlaid moving pictures or animation, redirection to a different URL,etc. The same targeting abilities also are applicable to networks thatare interactive to a lesser degree, such as cable television adinsertion, which might be done at a head end or at a hub, or even from asubscriber-specific set top box.

Accordingly, ad impressions may be delivered in a manner to targetcharacteristics (or attributes) of users or web page content—referred toas representativeness—with the goal to fairly represent the targetedcharacteristics in delivered impressions. When advertisers pay inadvance by way of contract for a specific number of ad impressions, theydesire a certain quality of representativeness, which creates what istermed guaranteed demand (GD). Normally, ad impressions are first servedto satisfy GD contracts. Ad impressions may also be auctioned via an adexchange on an ad hoc spot market when the ad impressions exceedprojected guaranteed demands or are more profitably auctioned, whichcreates non-guaranteed demand (NGD). Availability of NGD ad impressionsis generally resolved immediately before advertisement delivery in realtime.

Optimizing a balance between meeting the guaranteed (GD) demand in arepresentative way that best targets user characteristics with a desireto increase NGD revenue and minimize penalties associated withunder-delivery of GD ad impressions can be achieved through programmablemodeling.

BRIEF DESCRIPTION OF THE DRAWINGS

The system and/or method may be better understood with reference to thefollowing drawings and description. Non-limiting and non-exhaustivedescriptions are provided with reference to the following drawings. Thecomponents in the figures are not necessarily to scale, emphasis insteadbeing placed upon illustrating principles. In the figures, likereferenced numerals may refer to like parts throughout the differentfigures unless otherwise specified.

FIG. 1 is a block diagram of a general overview of a network environmentand system for distributing advertisement impressions.

FIG. 2 is a flow/block diagram illustrating a method and system tosupport a marketing relationship among advertisers, media outlets and anad distribution system.

FIG. 3 is a block diagram of an exemplary architecture for advertisingdelivery systems.

FIG. 4 is block diagram of another exemplary architecture foradvertising delivery systems.

FIG. 5 is a graphic depiction of a cost of supply function when supplyvolume of ad impressions fluctuates.

FIG. 6 is a graphic depiction of an under-delivery penalty function.

FIG. 7 is a graphic depiction of a non-guaranteed cost and revenuefunction.

FIG. 8 is a flow chart of a method for distributing advertisementimpressions through an exchange in which the number of ad impressions ischangeable.

FIG. 9 is an exemplary processing system for implementing theadvertisement impression distribution systems and methods.

DETAILED DESCRIPTION

A supply of ad impression opportunities preferably comprisesopportunities to insert on-line advertising (“ad impressions”), such asinserting variable banner ads into web pages that are transmitted tousers. The ads can be allocated selectively, based on characteristics ofthe user or typical users of the particular web page, or otherwiseselected to match user and content information, location, timing andother criteria to advertiser specifications, for targeting the ads topotential customers or web users.

The allocation of increments of a supply of advertisements to meetdemand may be optimized in a market for use of advertising opportunities(ad impressions) by establishing a proportion of revenue and/or quantityto be shared between distinct categories of demand with potentiallydifferent marginal values. A programmable technique divides allallocations that are projected and later the allocations that actuallyarise, between a category of pre-committed increments, typicallycontractually committed ad insertion opportunities with predeterminedcharacteristics (e.g., guaranteed delivery (GD) of ads contracts), and acategory of spot sales, such as via ad exchange auctions (e.g.,non-guaranteed delivery (NGD) delivery of ads).

To increase opportunities for revenue, the ad impression supply volumefor meeting all demands may be increased, for instance by broadening thecharacteristics targeted by advertisers. Other variables, such aschanging web page content or navigational links on web pages, maysimilarly cause the ad impressions to change in volume. Additionally, adimpressions may change when writers are solicited to write articlesabout a particular topic (e.g., via an associated content platform).

The supply volume of ad impressions, however, involves a cost whenincreased beyond a predetermined level, as will be discussed later inmore detail. For instance, if a threshold score for qualifying usersinto specified interest categories is changed such as to capture more adimpressions, the return on investment (ROI) advertisers see from adsdelivered to those impressions drops as the ad impressions becomeincreasingly less targeted to the profile of the advertiser. Deliveredads that are less targeted are lower in quality and will likely garnerfewer clicks, thus reducing the advertiser's click-through-rate (CTR) onaffected advertisements. The disclosed system may automate the processby which ad impressions are advantageously increased so as to generatemore revenue for publishers and brokers, such as Yahoo! of Sunnyvale,Calif., but to moderate that increase so as to minimize costs associatedwith changeable supply of ad impressions. Those costs may be seen inlost advertisers or fewer contracts with advertisers due to a reductionin their ROI, for instance, and thus may be viewed as lost opportunitycosts.

A multi-objective approach to optimizing representativeness is modeledherein that seeks to minimize the cost of a changeable volume of adimpressions and to minimize penalties due to under-delivery of adimpressions to satisfy GD contracts while maximizing, in a balancedapproach, both GD representativeness and NGD demand revenue. Thisoptimization will take place with reference to buying and selling adimpressions in an advertising marketplace including an ad exchange whereover-delivery and under-delivery are also modeled.

FIG. 1 provides a simplified view of a network environment 100 forserving advertisement impressions, factoring both guaranteed demand andnon-guaranteed demand, in an optimized way. Not all of the depictedcomponents may be required, however, and some implementations mayinclude additional components not shown in the Figure. Variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the claims as set forth herein. Additional,different or fewer components may be provided.

The network environment 100 may include an administrator 110 and one ormore users 120A-N with access to one or more networks 130, 135, and oneor more web applications, standalone applications, mobile applications115, 125A-N, which may collectively be referred to as clientapplications. The network environment 100 may also include one or moreadvertisement servers 140 and related data stores 145, and one or moreoptimizer servers 150 and related data stores 155. The users 120 A-N mayrequest pages, such as web pages, via the web application, standaloneapplication, mobile application 125 A-N, such as web browsers. Therequested page may request an advertisement impression from theadvertisement server 140 to fill a space on the page. The advertiserserver 140 may serve one or more advertisement impressions to the pagesin accordance with delivery instructions from the optimizer server 150.The advertisement impressions may include online graphicaladvertisements, such as in a unified marketplace for graphicaladvertisement impressions. Some or all of the advertisement server 140,the optimizer server 150, and the one or more web applications,standalone application, mobile applications 115, 125A-N, may be incommunication with each other by way of the networks 130 and 135.

The networks 130, 135 may include wide area networks (WAN), such as theInternet, local area networks (LAN), campus area networks, metropolitanarea networks, or any other networks that may allow for datacommunication. The network 130 may include the Internet and may includeall or part of network 135; network 135 may include all or part ofnetwork 130. The networks 130, 135 may be divided into sub-networks. Thesub-networks may allow access to all of the other components connectedto the networks 130, 135 in the network environment 100, or thesub-networks may restrict access between the components connected to thenetworks 130, 135. The network 135 may be regarded as a public orprivate network connection and may include, for example, a virtualprivate network or an encryption or other security mechanism employedover the public Internet, or the like.

The web applications, standalone applications and mobile applications115, 125A-N may be connected to the network 130 in any configurationthat supports data transfer. This may include a data connection to thenetwork 130 that may be wired or wireless. Any of the web applications,standalone applications and mobile applications 115, 125A-N mayindividually be referred to as a client application. The web application125A may run on any platform that supports web content, such as a webbrowser or a computer, a mobile phone, personal digital assistant (PDA),pager, network-enabled television, digital video recorder, such asTIVO®, automobile and/or any appliance or platform capable of datacommunications.

The standalone application 125B may run on a machine that includes aprocessor, memory, a display, a user interface and a communicationinterface. The processor may be operatively connected to the memory,display and the interfaces and may perform tasks at the request of thestandalone application 125B or the underlying operating system. Thememory may be capable of storing data including programmableinstructions or computer code. The display may be operatively connectedto the memory and the processor and may be capable of displayinginformation to the user B 125B. The user interface may be operativelyconnected to the memory, the processor, and the display and may becapable of interacting with a user B 120B. The communication interfacemay be operatively connected to the memory, and the processor, and maybe capable of communicating through the networks 130, 135 with theadvertisement server 140. The standalone application 125B may beprogrammed in any programming language that supports communicationprotocols. These languages may include: SUN JAVA®, C++, C#, ASP, SUNJAVASCRIPT®, asynchronous SUN JAVASCRIPT®, or ADOBE FLASH ACTIONSCRIPT®,ADOBE FLEX®, and others.

The mobile application 125N may run on any mobile device that may have adata connection. The data connection may be a cellular connection, awireless data connection, an internet connection, an infra-redconnection, a Bluetooth connection, or any other connection capable oftransmitting data. For example, the mobile application 125N may be anapplication running on an APPLE IPHONE®.

The advertisement server 140 may include one or more of the following:an application server, a mobile application server, a data store, adatabase server, and a middleware server. The advertisement server 140may exist on one machine or may be running in a distributedconfiguration on one or more machines. The advertisement server 140 maybe in communication with the client applications 115, 125A-N, such asover the networks 130, 135. For example, the advertisement server 140may provide a user interface to the users 120A-N through the clientapplications 125A-N, such as a user interface for inputting searchrequests and/or viewing web pages. Alternatively or in addition, theadvertisement server 140 may provide a user interface to theadministrator 110 via the client application 115, such as a userinterface for managing the data source 145 and/or configuringadvertisements.

The service provider server 140 and client applications 115, 125A-N maybe one or more computing devices of various kinds, such as the computingdevice in FIG. 9. Such computing devices may generally include anydevice that may be configured to perform computation and that may becapable of sending and receiving data communications by way of one ormore wired and/or wireless communication interfaces. Such devices may beconfigured to communicate in accordance with any of a variety of networkprotocols, including but not limited to protocols within theTransmission Control Protocol/Internet Protocol (TCP/IP) protocol suite.For example, the web application 125A may employ the Hypertext TransferProtocol (“HTTP”) to request information, such as a web page, from a webserver, which may be a process executing on the advertisement server140.

There may be several configurations of database servers, applicationservers, mobile application servers, and middleware applicationsincluded in the advertisement server 140. The data store 145 may be partof the advertisement server 140 and may be a database server, such asMICROSOFT SQL SERVER®, ORACLE®, IBM DB2®, SQLITE®, or any other databasesoftware, relational or otherwise. The application server may be APACHETOMCAT®, MICROSOFT IIS®, ADOBE COLDFUSION®, or any other applicationserver that supports communication protocols.

The networks 130, 135 may be configured to couple one computing deviceto another computing device to enable communication of data between thedevices. The networks 130, 135 may generally be enabled to employ anyform of machine-readable media for communicating information from onedevice to another. Each of the networks 130, 135 may include one or moreof a wireless network, a wired network, a local area network (LAN), awide area network (WAN), a direct connection such as through a UniversalSerial Bus (USB) port, and the like, and may include the set ofinterconnected networks that make up the Internet. The networks 130, 135may include communication methods by which information may travelbetween computing devices.

FIG. 2 is a flow/block diagram illustrating a method and system tosupport a marketing relationship among advertisers 200, media outlets230 and an ad distribution system 250 herein described. Inasmuch as thead impressions that are to be used to meet the representative demandprofile arise over time, an agreement to exploit the ad impressions mayrely partly on an estimation of the number and character of adimpressions that arise. If a media outlet is reasonably sure that agiven number of ad impressions of a given type arise, then the mediaoutlet can commit contractually to using the ad impressions to meet thedemand of particular advertisers whose representative profileencompasses ad impressions of that type. In an advertising contract, itis possible for parties to agree to a “best efforts” obligation toproduce exploitable ad impressions, but a contract containingobligations to produce a certain number and type of ad impressions maybe preferable. In that case, the guaranteed ad impressions (guaranteeddeliver GD ads) can command a better price than potential ad impressionsthat might be subject to contract but are not guaranteed (non-guaranteeddelivery NGD ads) and are uncertain to arise at all.

This situation may be handled in advertising systems by sellingguaranteed ad impressions in advance, and selling the additional adimpressions that may arise under different contractual provisions andeffectively in a substantially independent market. The present addistribution system is configured to aid in unifying these two differentmarkets.

An efficient and organized technique may satisfy the demand fordistribution of advertising to users. The users can be more or lessspecifically defined by user characteristics. From the advertisers'perspective, an objective is to enable ads to be targeted to users as afunction of the users' characteristics. The users' likelycharacteristics are known to the media outlets that serve the users, atleast because user characteristics correlate with the content of mediaoutlets that the users visit. Often the media outlets may have access toadditional subscriber information from browsing history, stored cookiesand other factors. The advertisers have preferences and rules fordistribution of ads, that may include guidelines based on likely usercharacteristics and also rules for spreading advertising coverage over arange of users. All such rules, guidelines and preferences on the partof an advertiser, which might result from studies and marketing plans,together define a representative profile of the advertising demand ofthat advertiser. An advertiser's representative demand profilecorresponds to a subset of all opportunities that might become availableto insert and display an ad (all the “ad impressions”), and may includesome insertions that are of more value to the advertiser than others.

Likewise, from knowledge of user characteristics and from projections ofthe likely range of users who may be interested enough to visit acertain type of media content in the future, the media outlets can makeestimates of the numbers and characteristics of users that are likely tobe subject to advertising impressions that might be devoted todisplaying an advertiser's content. There is a supply and demand marketinvolving discriminating for ad impressions that meet an advertiser'srepresentative demand profile, allocating and using the ad impressionsthat arise to meet incremental parts of the representative demand,reporting to the advertiser and collecting revenue in exchange for thisservice.

In FIG. 2, the advertisers 200 define a representative demand profilethat they deem to be appropriate. The advertisers 200 might study theirproducts, commission surveys, collect information from actual customersand so forth, to identify likely targets for ads for a particularproduct or ads written perhaps to be appealing to some recipients morethan others. The advertisers 200 typically have various rules forassociating ads with ad impressions of distinct types, and fordistributing ads generally over various subsets of a population, notnecessarily limited to applying their advertising expenditures only tocertain targeted subsets. All such rules and associations make up arepresentative profile that can be unique to an advertiser or anadvertised product.

The media outlets 230 also collect information about their user base andthe patterns of user access to and usage of media of one content oranother. The media outlets have knowledge of the content of the mediaand also have knowledge of their users' patterns of access. The mediaoutlets may have subscriber information such as location and demographicdata. Some subscriber information can be inferred from a user's accessto certain content. All this information is collected and used to studyand associate patterns of subscribers and content so as to provideknowledge of the opportunities available to insert advertising that maybe of interest to users.

The information collected by media outlets enables projections toestimate the nature and number of ad impressions that are likely tobecome available at a given time. The information can include, forexample, an estimated number of users having defined characteristics whoare projected to access a particular web page or other media contentsource over a given time window. Depending on the information collected,the defined user characteristics might include measures of age, gender,income, family associations, etc., with statistical ranges of confidencein the values.

As a result of collection and study of information, the media outlets ortheir nominee can determine and define a projected probable inventory ofad impressions that may be offered for sale to advertisers. Withinstatistical limits, the media outlets may believe that an excessinventory of ad impressions may be available. However, the media outletsare not likely to commit as readily to sale of ad impressions undercontracts guaranteeing delivery or containing non-performance penalties,when the availability of the excess ad impressions is unsure. The excessad impressions in that case might be sold on a spot market when itbecomes clear that the ad impressions are available, e.g., immediatelybefore the ad impressions might be used.

According to one aspect, the two markets are to be merged insofar aspossible, for ad impressions sold under guaranteed contracts and adimpressions sold only when they prove to be available. This isaccomplished in part by optimizing the selected proportion of adimpressions that are committed to guaranteed delivery contracts versusthe proportion that are sold if possible when excess ad impressionsprove to be available. This is also accomplished in part by providingcompetition between guaranteed and non-guaranteed demand whenaccomplishing the delivery of emerging ad impressions as the adimpressions become available. These steps are accomplished by the addistribution system 250 as an intermediary between the advertisers 200and media outlets 230.

The ad distribution system 250 functions to optimize the proportions orthe division of ad impressions that are allocated to guaranteedcontracts or to ad hoc spot sales. The optimization can be accomplishedduring negotiations as to whether to commit to guaranteed sales, but mayalso be accomplished repetitively by the ad distribution system 250 asconditions change over time. In addition to negotiations in advance, thead distribution system 250 matches demand increments of therepresentative profiles of advertiser versus projected probableopportunities to use ad impressions, and repetitively updates theprojections and rebalances the proportions that are used or planned foruse either to satisfy guaranteed obligations or to be sold on the ad hocspot market. The ad impressions are allocated as a function of price andperformance, namely to achieve: (1) a high likelihood that guaranteedobligations are met, (2) a close match of allocated ad impressions withthe representative profiles of the advertisers, and (3) allocation ofthe ad impressions to the users that achieve the highest revenue for themedia outlets.

FIGS. 3 and 4 are block diagrams illustrating exemplary architecturesfor advertising delivery systems 300. In FIG. 3, the advertisingdelivery system 300 is configured to integrate handling of determinedcommitments to provide ad impressions together with emergentopportunities to make spot sales. The determined commitments aregenerally termed “guaranteed” contracts or guaranteed delivery (GD) ofad impressions, but the idea of guaranteed contracts encompasses anycommitment entered before the moment of allocation of an ad impressionto a demand, when the allocation reduces the total supply of remainingad impressions that are available, and thus reduces the number of adimpressions that might yet be committed to another guaranteed contractor might be allocated for spot sales up to the last possible moment.

The advertising delivery system 300 can be embodied as a service of aprogrammed network server that manages the allocation of the supply ofad impressions available from subscribing website operators and similarmedia outlets versus the demand by advertisers to use the adimpressions, optionally providing the interface through which ad contentis routed to the media outlets for insertion, as windows, banners andother elements of web pages being composed for display by the respectivebrowser programs that compose the web pages for viewing by users, e.g.,when surfing the Worldwide Web. In an advantageous embodiment supportedby user interfaces for the advertisers and media distributors oroutlets, the system, including methods, can be configured to manageallocation of guaranteed-delivery ad impressions in a number projectedby media distributors to be available, and also to manage the offeringand ad hoc sale of excess ad impressions that are found to be availablebeyond those that were projected. These excess impressions can be soldat auction and used up to the time at which it becomes apparent that thenumber of impressions in the actual supply exceed what was projected.Alternatively or in addition, impressions to be sold at auction do nothave to be excess impressions. For example, if the projected auctionprice is higher than the under-delivery penalty cost, it may be moreprofitable to sell the impressions at auction, whether or not they arein excess to the projected guaranteed ad impressions.

The advertising delivery system 300 in FIG. 3 can unify the allocationand sale of ads, eliminating artificial separation between the adimpression inventory that is sold months in advance under agreementsentailing guaranteed delivery (i.e., obligations as to the number andnature of impressions and potential penalties for inability to deliver)versus the remaining inventory, normally from overly-conservativeestimates and projections, to be sold using a real-time auction, spotmarket or terms of “best efforts” non-guaranteed delivery (NGD).

The advertising delivery system 300 manages advertising to servecontracts (i.e., guaranteed ad impression deliveries) and non-guaranteed(NGD) contracts. As a result, high-quality or most sought afterimpressions are allocated to the guaranteed contracts and non-guaranteedcontracts. This mode for ad impression allocation may realize the fullpotential of the additional ad impressions that are available when thenumber of ad impressions proves to be greater than the number that wasprojected. By automated allocation and management of non-guaranteeddelivery impressions, including allocation and contractual commitment ofad impressions immediately prior to the time that the impressions becomeavailable, a mix of guaranteed and also non-guaranteed contracts canform a unified marketplace whereby an impression can be allocated to aguaranteed or non-guaranteed contract efficiently, based on the value ofthe impression to the different contracts, and with less value risked onthe ability to project ad impression availability far in advance. Aunified marketplace for long term (guaranteed) impressions and shortterm ones as well, enables equitable allocation of ad impressioninventory, and promotes increased competition between guaranteed andnon-guaranteed contracts.

One aspect of the ad delivery system 300 is a bidding mechanism thatenables guaranteed contracts to bid on the spot-market for eachimpression and compete directly with non-guaranteed contracts, whilestill meeting the guaranteed goals for the contracts. This competitionis facilitated if the value of ad impressions on the spot market issubject to highly refined targeting. For example, a selection of adimpressions targeted to “one million Yahoo! Finance users from 1 Aug.2008-31 Aug. 2008” is diluted and potentially less valuable to certainadvertisers compared to “100,000 Yahoo! Finance users from 1 Aug. 2008-8Aug. 2008 who are males between the ages of 20-35 located in California,who work in the healthcare industry and have recently accessedinformation on sports and autos.”

In order to shift to refined targeting, the advertising industry needsto forecast future ad impression inventory to a fine-grained level oftargeting, e.g., numerous variables with tight ranges or close adherenceto examples. Advantageously, correlations between different targetingattributes are identified and exploited by producing correlated variablevalues that can be compared directly to match ad impressions withdemand. Taken to a very fine level, it may be appropriate to managecontention in a high-dimensional targeting space with hundreds tothousands of targeting attributes because different advertisers canspecify different overlapping targeting combinations. If numeroustargeting combinations are accepted and guaranteed, the advertisingdelivery system 300 may help ensure that sufficient inventory isavailable, while minimizing a supply cost associated with an increase inad impressions.

In FIG. 3, the advertising delivery system 300 coordinates the executionof various system components, operating as a server with severalsubsystems devoted to arranging for handling the contractual matching ofguaranteed ad impressions allocated to demands according to projections,plus spot market sales of ad impressions that become available, andserving ads to fill the ad impressions.

An admission control and pricing sub-system 302 facilitates guaranteedad contracts, preferably for a time period up to a year in advance ofactual presentation of ad impressions that are contracted. Thissub-system 302 assists in pricing guaranteed contracts, and is coupledto supply and demand forecasting subsystems for this purpose.

An ad serving sub-system 304 has a subsystem that matches ad guarantees(demands) with opportunities (ad impressions), including serving theguaranteed impressions and also through ad hoc bidding system wherebyselected guaranteed impressions may be supplied by deals on the spotmarket at favorable terms.

The admission control module 302 has input and output signal paths forinteracting with sales persons who negotiate and contract withadvertisers. A sales person may issue a query that defines a specifiedtarget (e.g., “Yahoo! finance users who are California males who likesports and autos”) and the Admission Control module determines andreports the available inventory of ad impressions for the target and theassociated price. The sales person can then book a contract accordingly.

The ad server module 304 takes on an ad impression opportunity, whichcomprises a user such as a web page viewer and a context, such as a URLfor the visited page and information on the theme of the content of theweb page begin viewed. Other information useful for targeting may beavailable, such as the succession of URLs visited by the user prior tothe visited page. The ad server module 304 returns a guaranteed ad tofill the ad impression opportunity, and determines an amount that thesystem is willing to bid for that opportunity in the spot market (an adexchange 306).

The operation of the ad delivery system 300 is orchestrated by anoptimization module 310. This module periodically takes into account aforecast of supply (future impressions that are projected), futureguaranteed demand (projected guaranteed contracts) and non-guaranteeddemand (expected bids in the spot market) that are generated from asupply forecasting module 313, and two demand forecasting modules 315,317 that are arranged to distinguish between guaranteed andnon-guaranteed demand elements. However, as ad impressions are madeavailable, the system can decide whether to use the ad impression tosatisfy the guaranteed commitments or to apply them to the spot market.

The optimization module 310 matches supply to demand using an overallobjective function as described herein, namely matching instances of adimpressions (supply) to meet instances of demand according to theadvertisers' representative profiles of demand, preferably using a normfunction that matches supply and demand according to the distancebetween the variable values of the supply and demand instance attributesin multi-dimensional space. The optimization module 310 sends a summaryplan characterizing the optimization results to the admission controland pricing module 302 and to a plan distribution and statisticsgathering module 312. The plan distribution and statistics gatheringmodule 312 sends information defining the plan to the ad servers 304.The plan produced by the optimization module can be updated periodicallyas estimates for supply, demand, and delivered impressions areavailable, e.g., every few hours.

Given the plan, the admission control and pricing module 302 works asfollows. When a sales person issues a targeting query for some durationin the future, the system first invokes the supply forecasting module313 to identify how much inventory is available for that target andduration. As mentioned earlier, targeting queries can be veryfine-grained, thus having numerous values in a multi-dimensional spacehaving numerous coordinate axes. The supply forecasting module uses ascalable multi-dimensional database indexing technique for this purpose,with bit-map indices, to enable correlations between different targetingattributes so that the values of instances of supply and demand havesome coordinate axes in common, and so that where values are unknown, astatistical probability may be available either to infer a likely valueor to dictate that the representative profile should entail distributingsupply or demand instances over a range of values for a coordinate axis.

Generating values on the coordinate axes for supply and demand instancesis only a part of the larger problem of allocating supply and demandbecause there is contention between alternative demands for the sameinstance of supply and vice versa. For example, if there are two demandcontracts: “Yahoo! finance users who are California males” and “Yahoo!users who are aged 20-35 and interested in sports,” it may beadvantageous to take into account the correlation between the demandinstances to avoid double-counting, in this example because maleCalifornia finance users may have a high correlation with that agebracket and with an interest in sports.

In order to deal with this contention problem in a high-dimensionalspace, a supply forecasting system preferably computes the match betweensupply instances as impression samples as opposed to a raw count ofavailable ad impressions. The samples of impressions are used as inputsto compute whether multiple demand contracts are connected to theattributes of a given impression.

Given the impression samples, the admission control module 302 uses theplan communicated by the optimization module 312 to calculate thecontention between contracts in the high-dimensional space, and returnsan available inventory measure to the sales persons withoutdouble-counting. In addition, the admission control module 302calculates a proposed price for each contract and returns that alongwith the quantity of available impressions.

Given the plan, the ad server module 304 works as follows. When anopportunity is presented, for example because a user's browser isengaged in generating the display of a web page from HTML data andencounters a graphic that is linked to a web address associated with thead server, an IP call is made for associated media content (e.g., text,graphics, animation, etc.). The ad server module 304 calculates thecontention among contracts for this impression in a manner similar towhat is done by the admission control and pricing module 302 whendetermining contractual terms beforehand. Given this instance of anavailable ad impression, and with contention information and knowledgeabout non-guaranteed demands, the ad server module 304 responds byselecting a contract with an instance to be filled. The ad server module304 generates a bid that serves to evaluate the contract, and sendsinformation on the contract and the bid to the exchange element 306. Itis then possible for the exchange to associate an instance of anon-guaranteed contract, e.g., to sell the ad impression rather than tofill it in satisfaction of the guaranteed contract that is in hand. Ifthe ad impression is sold, the ad server can return content providedfrom the buyer through the exchange module 306. If the terms availableover the exchange are less favorable, the ad server 304 returns thecontent associated with the guaranteed demand instance.

Matching a given set of contracts—representative profile demands havingvalues associated with various variables—versus and a set of impressionsamples—ad impression instances of supply, also having values associatedwith various values—is a core task, and is served substantially by theoptimization module 310. The task is to decide how to allocate theprojected or available ad impressions to satisfy the specifications ofthe demand contracts. One of the goals is a representative allocation.When a contract demand might be satisfied by multiple eligible adimpression types, each of which would contribute in some degree tomeeting the demand, it is desirable to allocate some volume of eacheligible impression type to corresponding contract demands. In short, itis desirable to allocate supply instances that have a given set ofattribute values, to favor targets who have matched attribute values,but not to allocate all the supply to targets based on one attributevalue at the expense of others. It is desirable to spread the allocationvolume in a manner that is related to the number of instances of allimpression types and demand instances, for example proportionately.Advantageously, the allocation favors but does not serve exclusively,those matches wherein certain variable attributes are close in value(i.e., the viewer in context closely meets one of several measurestargeted) at the expense of other attributes.

In the unified marketplace, there are two competing sources of demand towhich a particular ad impression might be allocated. An ad impressionmight be used to satisfy a guaranteed delivery (GD) obligation under acontract, or might be sold on the non-guaranteed delivery (NGD) spotmarket. In a market that is not unified, and assuming that there wassufficient demand from advertisers at the prices offered, the adimpressions of the media distributors might be contractually guaranteedonly insofar as their projected availability has a high level ofconfidence. It is an aspect of the present technique, however, not toallocate only on confidence in availability but instead to seek tomaximize the efficiency and value of the allocation to both portions ofthe demand. Accordingly, the optimization module is used to seek anefficient division of allocations between the guaranteed and spotmarkets.

When impressions are allocated to guaranteed delivery contracts, therepresentativeness of the allocation is the major goal, namely toclosely match the allocation to the number and type of ad impressionsthat define the representative profiles of the advertisers. On the otherhand, when ad impressions are sold on the non-guaranteed delivery spotmarket, the goal is merely revenue.

The total available ad impressions are a finite supply. If an impressionis allocated to guaranteed delivery, that impression is not availablefor non-guaranteed demand, and vice versa. The marginal revenue thatmight be obtained from sale of an ad impression on the spot markettherefore is compared directly with the marginal value of using thatsame ad impression to satisfy a guaranteed delivery obligation. Asexplained herein, this gives a basis in which to make reasoned decisionsas to what proportion of available ad impressions should mostefficiently be devoted to meeting guaranteed delivery obligations andwhat proportion should be sold on the ad hoc spot market, for example atauction. Such decisions are enabled in the optimization module 310.

The marginal revenue from a spot market sale of an ad impression is alost opportunity that is comparable to a cost for a guaranteed deliveryallocation of that ad impression. One task of the optimization module310 is to decide how to balance the allocations between guaranteeddelivery contracts and the non-guaranteed delivery spot market toachieve efficiency and other business goals. Another task of theoptimization module 310 is to minimize a supply cost associated with achangeable number of ad impressions.

The question of whether to allocate to guaranteed or non-guaranteedallocations is regarded herein as a multi-objective optimization problemwith the number and marginal revenue of both allocation categoriescontributing to a common total but their respective contributionscompeting for the available supply. Both guaranteed delivery value(which equates to representativeness) and non-guaranteed delivery marketrevenue (which as an opportunity cost can be assessed against guaranteeddelivery value) are modeled explicitly as described herein. Modeling inthis way provides a framework to test the results of different functionsfor evaluating representativeness, enabling the model to identify acorresponding efficient allocation between guaranteed and spot marketallocations. The model effectively provides business controls that whenimposed on a mathematical optimization that produces a trajectory orrange of potential control points, establishes one point in the range tobe used as the basis of control. This result accrues using a methodologythat establishes a monetary value equivalent to the value ofrepresentativeness, for use in solving a multi-objective optimizationproblem.

FIG. 4 is a block diagram of an alternate architecture for theadvertising delivery system 300. An optimizer 310 utilizes inputs fromthe supply forecasting module 313, the guaranteed demand forecastingmodule 315, and the non-guaranteed demand forecasting modules 317.Supply forecasting 313 provides forecast ad opportunities (impressions)from which the optimizer 310 may determine a cost associated with asupply volume of the ad impressions, which will be discussed in moredetail later. Guaranteed demand forecasting 315 provides forecastcontracts and non-guaranteed demand forecasting 317 provides forecastnon-guaranteed demand prices. The optimizer 310 uses the inputs to runoptimization, such as described with regard to the algorithms below andwith reference to FIG. 3, to generate an ad allocation plan.

The optimizer 310 allocates the plans to both admission control 302 andthe ad server 304. The admission control 302 uses the allocation plan tocalculate inventory level to decide whether or not to accept a bookingquery. The ad server 304 uses the allocation plan to decide whether toallocate an incoming ad opportunity to serve a guaranteed contract orsell the ad opportunity to the non-guaranteed marketplace. The ad server304 serves an advertisement to an application 125A-M, such as a browser,of user 120A-N in accordance with the plan. The optimizer 310 executesallocation optimization algorithms periodically, such as when inputs areupdated with all the forecasts as well as feedback of newly-bookedcontracts from the admission control 302 and advertisement deliverystatistics from the ad server 304.

Algorithms, such as those used by the optimizer 310 of the system 300,are modeled mathematically and shown graphically using the variableslisted and defined in Table I:

TABLE I i: Index of ad opportunity (supply), i = 1, . . . , I j: Indexof guaranteed contract (demand), j = 1, . . . , J s_(i): Supply volumeof supply i d_(j): Demand volume of demand j r_(i): Non-guaranteed price(opportunity cost) of supply i • Splits to r_(i) ^(b) and r_(i) ^(s)when both buying and selling are modeled v_(j): Allocation priority ofdemand j b_(jk): Break points of piecewise linear penalty function fordemand j c_(j) Under-delivery penalty cost for demand j • For piecewiselinear penalty function, c_(j) = (c_(j1), . . . c_(jKj)) B_(j): Set ofsupplies that are eligible to serve demand j S_(j): Total supply volumethat is eligible for demand j, e.g., S_(j) = Σ_(iεB) _(j) _(s) ^(i)θ_(ij): Proportional allocation from supply i to demand j, e.g.,  $\theta_{ij} = {\frac{d_{j}}{S_{j}}S_{i}}$ x_(ij): (variable) Allocationvolume from supply i to demand j y_(i): (variable) Volume of supply isold in the NGD marketplace • Splits to y_(i) ^(b) and y_(i) ^(s) whenboth buying and selling are modeled z_(j) (variable) Under-deliveryvolume of demand j • For piecewise linear penalty function, z_(j) =(z_(j1), . . . z_(jKj))

The advertising inventory allocation problem can be modeled as thefollowing multi-objective allocation model.

$\begin{matrix}{\min\begin{bmatrix}{{f_{1}(z)} = {\sum\limits_{j}{\sum\limits_{k}{c_{jk}z_{jk}}}}} \\{{f_{2}(y)} = {- {\sum\limits_{i}{r_{i}y_{i}}}}} \\{{f_{3}\left( {x;\theta} \right)} = {\sum\limits_{j}{\frac{1}{2}{\sum\limits_{i}{\frac{vj}{\theta_{ij}}\left( {x_{ij} - \theta_{ij}} \right)^{2}}}}}}\end{bmatrix}} & (1) \\{{{s.t.\mspace{14mu}{\sum\limits_{j|{i \in B_{j}}}x_{ij}}} + y_{i}} = {s_{i}\mspace{14mu}{\forall i}}} & (2) \\{{{\sum\limits_{i \in B_{j}}x_{ij}} + {\sum\limits_{k}z_{jk}}} = {d_{j}\mspace{14mu}{\forall j}}} & (3) \\{x_{ij} \geq {0\mspace{14mu}\forall_{i,j}}} & (4) \\{y_{i} \geq {0\mspace{14mu}{\forall i}}} & (5) \\{{0 \leq z_{jk} \leq {b_{j,{k - 1}}\mspace{14mu}{\forall j}}},{k \geq 1}} & (6) \\{z_{j\; 0} \leq 0} & (7)\end{matrix}$

There are three objectives in this model, where ƒ₁(z) is theunder-delivery penalty cost, ƒ₂(y) is the negative NGD revenue whereminimizing ƒ₂(y) is equivalent to maximizing the NGD revenue, andƒ₃(x;θ) is the representativeness of allocation for GD contracts. Theseconstraints describe the basic network flow of the optimization model,e.g., for each supply node, the total allocation to both GD contractsand NGD market must equal the supply volume; and for each demand node,the total allocation from all eligible supply nodes plus theunder-delivery volume (if any) must equal the demand volume.

When the supply of ad impressions is changeable, the inflated supplyvolume is associated with a cost. Assume that the cost of supply i canbe modeled by a piece-wise linear convex function as shown in FIG. 5,where0=m _(i0) ≦m _(il) ≦ . . . ≦b _(i,L) _(i) ₊₁=∞  (8)are the break points,0=q _(i0) ≦q _(il) ≦ . . . ≦b _(iLi)  (9)are the costs associated with each segment of the cost function ands _(i)=(s _(i0) , . . . ,s _(iLi))  (10)is the variable of the supply volume. Note that the break point m_(il)represents the threshold of the supply volume, below which the cost ofsupply is zero. Any supply volume above the threshold is associated witha cost. Adding this cost as ƒ₄(s) into the multi-objective allocationmodel of equation (1), the model may be recast as:

$\begin{matrix}{\min\begin{bmatrix}{{f_{1}(z)} = {\sum\limits_{j}{\sum\limits_{k}{c_{jk}z_{jk}}}}} \\{{f_{2}(y)} = {- {\sum\limits_{i}{r_{i}y_{i}}}}} \\{{f_{3}\left( {x;\theta} \right)} = {\sum\limits_{j}{\frac{1}{2}{\sum\limits_{i}{\frac{vj}{\theta_{ij}}\left( {x_{ij} - \theta_{ij}} \right)^{2}}}}}} \\{{f_{4}(s)} = {\sum\limits_{i}{\sum\limits_{l}{m_{il}s_{il}}}}}\end{bmatrix}} & (11) \\{{{s.t.\mspace{14mu}{\sum\limits_{j|{i \in B_{j}}}x_{ij}}} + y_{i}} = {\sum\limits_{l}{m_{il}s_{il}\mspace{14mu}{\forall i}}}} & (12) \\{{{\sum\limits_{i \in B_{j}}x_{ij}} + {\sum\limits_{k}z_{jk}}} = {d_{j}\mspace{14mu}{\forall j}}} & (13) \\{x_{ij} \geq {0\mspace{14mu}\forall_{i,j}}} & (14) \\{y_{i} \geq {0\mspace{14mu}{\forall i}}} & (15) \\{{0 \leq z_{jk} \leq {b_{jk} - {b_{j,{k - 1}}\mspace{14mu}{\forall j}}}},k} & (16) \\{{0 \leq s_{il} \leq {m_{i,{l + 1}} - {m_{i,l}\mspace{14mu}{\forall j}}}},l} & (17)\end{matrix}$

The GD (guaranteed demand) representativeness utility function isassumed to be separate in terms of each demand j.

${f_{3}\left( {x,\theta} \right)} = {\sum\limits_{j}{f_{3}^{j}\left( {x_{j},\theta_{j}} \right)}}$

The function term ƒ₃ ^(j)(x_(j),θ_(j)) can be formulated in differentways. A few examples are listed as follows.

Base on L₂ distance

${f_{3}^{j}\left( {x_{j},\theta_{j}} \right)} = {\frac{1}{2}{\sum\limits_{i}{\frac{v_{j}}{\theta_{ij}}\left( {x_{ij} - \theta_{ij}} \right)^{2}}}}$Base on L₁ distance

${f_{3}^{j}\left( {x_{j},\theta_{j}} \right)} = {\sum\limits_{i}{\frac{v_{j}}{\theta_{ij}}\left( {x_{ij} - \theta_{ij}} \right)}}$Base on L_(∞) distance

${f_{3}^{j}\left( {x_{j},\theta_{j}} \right)} = {\max\limits_{i}{\frac{v_{j}}{\theta_{ij}}{{x_{ij} - \theta_{ij}}}}}$Base on K-L divergence

${f_{3}^{j}\left( {x_{j},\theta_{j}} \right)} = {\sum\limits_{i}{v_{j}x_{ij}{\log\left( \frac{x_{ij}}{\theta_{ij}} \right)}}}$

As a reminder, the multi-objective model may include multipleobjectives, such as the following four objectives:

-   -   1. minimize under-delivery penalty of GD contracts;    -   2. maximize NGD revenue (or equivalently, minimize NGD cost);    -   3. minimize a cost of the advertisement impressions (or supply)        when the supply volume fluctuates; and    -   4. maximize GD representativeness.

The four objectives may be interrelated such that to achieve maximum NGDrevenue by serving ad impressions to the NGD market, GDrepresentativeness may decrease. Likewise, to serve ad impressions tomaximize GD representativeness, that may impact NGD revenue. There mayalso be tradeoffs between under-delivery penalties and NGD revenue. Ifenough ad impressions do not exist to satisfy all the GD contracts, anunder-delivery penalty may apply. In the alternative, or in addition,the number advertisement impressions may be increased to satisfy all theGD contracts while minimizing a cost as a result of making such a changeto the number of ad impressions.

Furthermore, an added revenue from serving an ad impression to the NGDmarket instead of the GD market may be greater than the under-deliverypenalty, which may be acceptable if a volume of the ad impressionscannot be increased due to minimization of the cost of changing supplyvolume. The system may also buy ad impressions from the market. In thiscase, the system may minimize the NGD cost. The system may also allowfor over-delivery to the market. The following approach may be used tohelp determine whether to serve a particular ad impression to an NGDmarket or a GD market, and which GD market to serve it to. The followingmay also be used to minimize a cost of changing the supply of adimpressions, and therefore balance the benefits of being able to delivermore ad impressions to both the GD and NGD markets while avoiding lossof advertiser revenues due to decreasing ad impression quality.

A solution for a multi-objective program may be referred to as efficientif there is no other solution that is equal or better in all objectivesand is better in at least one objective. All the efficient solutionsconstitute the efficient frontier. Solving the multi-objectiveoptimization problem seeks to find a solution on the efficient frontiercorresponding to specified priority or preference between theobjectives. There are different approaches to the problem to get anefficient solution.

One approach is to optimize the weighted sum of objectives:min w ₁ƒ₁(z)+w ₂ƒ₂(y)+w ₃ƒ₃(x;θ)+w ₄ƒ₄(s)  (18)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (19)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (20)x _(ij)≧0∀i,j  (21)y _(i)≦0∀i  (22)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (23)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (24)

where w=(w₁, w₂, w₃, w₄)≧0,Σ₁ ⁴w₁=1 are the weights for the relativepriority of the objectives. A higher prioritized objective should besolved first. It may, however, be difficult to set the weights to trulyrepresent the priority. For example, to set the right weight, the systemneeds to determine how much one unit of representativeness is worth interms of a dollar amount. Although the under-delivery penalty ƒ₁(z), theNGD revenue ƒ₂(y) and supply cost ƒ₄(s) may be in terms of monetaryvalues and thus easily compared to each other, the representativenessutility ƒ₃(x;θ) has abstract mathematical meaning (distance to theproportional allocation), and it is thus not straightforward to comparethe representativeness with the other objectives.

Another approach to multi-objective optimization is via goalprogramming. The system may solve each objective one by one, by addingconstraints on the values of one or more previously-solved objectives,which are also referred to herein as requirements. No specific order isnecessary as the below steps may be taken in different orders so long asat least one requirement determined from solving another objectiveincludes a new constraint. Accordingly, the below is but one example ofa possible sequence of steps in the goal programming algorithm. Imposinga new constraint on a value of a requirement may also be referred to asrelaxing the earlier-solved requirement to allow for less than anoptimum value for that requirement. Such relaxing earlier-solvedrequirements may result in a more balanced allocation plan in terms ofdelivering ad impressions between GD contracts and in the NGD market.

Step 1: Minimize Under-Delivery Penaltymin ƒ₁(z)=Σ_(j)Σ_(k) c _(jk) z _(jk)  (25)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (26)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (27)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (28)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (29)

No extra constraints are added in this step or any of the steps ifexecuted first in the sequential order of algorithmic steps.

Step 2: Minimize NGD Cost (Maximize NGD Revenue)min ƒ₂(y)=−Σ_(i) r _(i) y _(i)  (30)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (31)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (32)ƒ₁(z)≦(1+η₁)ƒ₁*  (33)y _(i)≧0∀i  (34)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (35)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (36)

where ƒ₁* is the optimum objective value of total under-delivery penaltycost from the first step and η₁≧0 is the percentage of ƒ₁* to relax. Forexample, if the optimum (minimum) under-delivery penalty cost is $1,000and η₁=0.9, the model can afford the under-delivery penalty cost up to$1,100 so as to maximize the NGD revenue.

Step 3: Minimize Ad Impression Supply Costmin ƒ₄(s)=Σ_(i)Σ_(l) m _(il) s _(il)  (37)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (38)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (39)ƒ₁(z)≦(1+η₁)ƒ₁*  (40)ƒ₂(y)≦(1+η₂)ƒ₂*  (41)y _(i)≧0∀i  (42)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (43)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (44)

where ƒ₁* and ƒ₂* are the optimum objective values of totalunder-delivery penalty cost and NGD revenue from the last two steps,respectively, and η₁≧0 and η₂≧0 are the percentages of ƒ₁* and ƒ₂* torelax, respectively. For example, if the optimum (minimum)under-delivery penalty cost is $1,000, the optimum (maximum) NGD revenueis $1,000,000, where η₁=0.9, and η₂=0.9, the model can afford themaximum under-delivery penalty cost of $1,100 and the minimum NGDrevenue of $900,000 so as to minimize the ad impression supply cost.

Step 4: Maximize GD Representativenessmin ƒ₃(x;θ)  (45)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (46)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (47)ƒ₁(z)≦(1+η₁)ƒ₁*  (48)ƒ₂(y)≦(1+η₂)ƒ₂*  (49)ƒ₄(s)≦(1+η₄)ƒ₄*  (50)x _(ij)≧0∀i,j  (51)y _(i)≧0∀i  (52)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (53)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (54)

where ƒ₁*, ƒ₂* and ƒ₄* are the optimum objective values of totalunder-delivery penalty cost, NGD revenue and supply cost from the lastthree steps, respectively, and η₁≧0, η₂≧0, and η₄≧0 are the percentagesof ƒ₁*, ƒ₂*, and ƒ₄* to relax, respectively. For example, if the optimum(minimum) under-delivery penalty cost is $1,000, the optimum (maximum)NGD revenue is $1,000,000, the optimum (minimum) supply cost is $2,000,where η₁=0.9, η₂=0.9, η₄=0.95, the model can afford the maximumunder-delivery penalty cost of $1,100, the minimum NGD revenue of$900,000, and the maximum supply cost of $2,100 so as to maximize the GDrepresentativeness.

When the under-delivery penalty ƒ₁(z), the NGD revenue ƒ₂(y), and ƒ₄(s)are all of monetary values, it is meaningful to combine them usingweighted sum. The optimal monetary objective value of the combinedfunction can then be used as a guideline for the non-monetary objectiveof representativeness.

Step 1: Optimize Monetary Objectivesmin ƒ₁₂₄(y,z,s)=w ₁Σ_(j)Σ_(k) c _(jk) z _(jk) −w ₂Σ_(i) r _(i) y _(i) +w₄Σ_(i)Σ_(l) m _(il) s _(il)  (55)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (56)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (57)y _(i)≧0∀i  (58)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (59)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (60)

Step 2: Optimize Non-Monetary Objectivesmin ƒ₃(x;θ)  (61)s.t. Σ _(j|iεB) _(j) x _(ij) +y _(i)=Σ_(l) m _(il) s _(il) ∀i  (62)Σ_(iεB) _(j) x _(ij)+Σ_(k) z _(jk) =d _(j) ∀j  (63)ƒ₁₂₄(y,z,s)≦(1+η₁₂₄)ƒ₁₂₄*  (64)x _(ij)≧0∀i,j  (65)y _(i)≧0∀i  (66)0≦z _(jk) ≦b _(jk) −b _(j,k-1) ∀j,k  (67)0≦s _(il) ≦m _(i,l+1) −m _(i,l) ∀i,l  (68)

where ƒ₁₂₄* is the optimal objective value of monetary cost from thefirst step and η₁₂≧0 are the percentages of ƒ₁₂₄* to relax.

The supply cost function ƒ₄(s) above is assumed to be piece-wise linearand convex, which enables the minimization of supply cost to be modeledas continuous linear programming (LP). In general, the supply costfunction may not be convex, in which case integer variables would haveto be introduced, resulting in a mixed integer problem that is hard tosolve for a large-scale model.

Note that the supply volume s_(i)=(s_(i0), . . . , s_(iL) _(i) ) is thevariable of supply volume. Since the cost associated with the firstsegment of the cost function, q_(i0)=0, the maximization of NGD revenuewill force the solution s_(i0)=m_(i1). The solution of the remainingelements of s_(i) will depend on the trade-off among the objectives.However, the convexity assumption of the supply cost function willguarantee thats _(il) <m _(i,l+1) −m _(i,l)

s _(i,l+1)=0.  (69)

The advertising distribution system as disclosed mediates anddistributes advertising opportunities, especially insertions of ads onweb pages, according to representative targeting profiles ofadvertisers. The number and characteristics of future ad impressions isforecast. A portion is allocated to guaranteed-delivery advertisercontracts and the remainder is offered on a spot market. A divisionbetween guaranteed and spot market allocations is sought to maximizerevenue, taking into account a value associated with meeting therepresentative profiles of advertisers and the quality of the adimpressions available for delivery. The value of representativeness canbe inferred from the marginal revenue of a spot market sale, andoptionally weighted.

The techniques as described are not limited to an Internet basedadvertising distribution system and can be applied to other instanceswhere there is a need to allocate supply and demand while deliveringvalue in exchange for revenue wherein the demand increments fall intocategories having at least one of quantities and revenues that differbetween the categories. Inasmuch there are totals of quantity andrevenue, it is known that an allocation to one category reduces theallocation to the other category. A relationship can be projected asdescribed that demonstrates the quantities and revenues that result fromallocating the total supply more or less to one or the other of the atleast two distinct categories, from zero to 100% or at least from zeroto a maximum proportion of the total supply. What remains is todetermine the operating point.

One or more goals may be imposed on the relationship in addition toaccounting for distribution of all the supply to one or the other of theallocation categories. The goal helps to determine a point in therelationship curve that corresponds to a particular proportionateallocation. The supply increments are then allocated to the demandincrements at this particular proportion in at least one of a plannedallocation and an actual allocation including delivering the supplyincrements. This allocation can be used when planning the proportion ofprojected ad impressions devoted to guaranteed delivery contracts, orcan be used when deciding how to use the successive ad impressions thatprove to be available, for example when web page hits occur enabling thetransmission of ad copy for insertion into the web page as rendered.

The disclosed allocation technique can incorporate functions thatcalculate the value of representativeness so as to rate the extent towhich emerging ads meet advertiser representativeness specifications,e.g., functions that allow a comparison of ad impression characteristicsand advertiser specifications as a measure of quality. Alternatively,the allocation can be based on an inferred monetary value based on theopportunity cost of employing an ad impression to meet a guaranteeddemand. The opportunity cost is at least equal to the amount that the adimpression would bring in on the spot market. It is advantageous,however, to weight the importance of representativeness versus revenue,preferably to assume that a high degree of representativeness (high adquality from the viewpoint of the advertiser) is a desired aspect forthe ad distribution service to deliver. Weighting can be accomplished bya factor that favor representativeness or by choosing a proportion ofrevenue that should be attributable to representativeness, and thuscontributes to long term customer goodwill.

FIG. 8 is a flow chart of a method for distributing advertisementimpressions through an exchange in which the number of ad impressions ischangeable. At block 400, an ad delivery or distribution systemestablishes a relationship between delivery of ad impressions toguaranteed (GD) contract demand and to non-guaranteed (NGD) demand on anadvertisement spot market, such as through an ad exchange auction. Therelationship defines a range of possible proportions of allocation ofthe ad impressions between GD and NGD demand. At block 410, the systemimposes one or more objectives on the relationship between allocation ofad impressions between GD and NGD demand. The objectives may include oneor more of: (1) minimizing a supply cost of the ad impressions; (2)maximizing NGD demand revenue; (3) minimizing under-delivery penalties;and (4) maximizing guaranteed (GD) demand representativeness. Otherobjectives are envisioned.

To minimize the supply cost, the system may moderate an increase in thenumber of ad impressions available for allocation, to minimize a costassociated with reducing a quality of the ad impressions as their volumeincreases. By way of implementation, the number of ad impressions may bemoderated when the ad impressions change based on one or more events.These events may include, but are not limited, to: (1) changing a scorethreshold for qualifying a user into a specified interest category suchas for behavioral targeting (BT) of users; (2) changing navigationallinks on a web page; and (3) dynamically changing displayed content on aweb page. Other events are envisioned.

Ad distribution may be optimized through goal programming. At block 420,the system solves for a first of the objectives to generate a firstrequirement. At block 430, the system may relax the first requirementwhile solving for a second of the objectives to generate a secondrequirement. The second requirement therefore is affected by relaxingthe first requirement. To relax a requirement may be viewed as thesystem allowing departure from its solved-for optimum value.Accordingly, relaxing a requirement that maximizes the objective is toallow the solved-for requirement to be less than the maximum value. Incontrast, relaxing a requirement that minimizes the objective is toallow the solved-for requirement to be more than the minimum value.

At block 440, the system may relax the first and/or the secondrequirements while solving for a third of the objectives to generate athird requirement. The third requirement is therefore affected byrelaxing the first and/or the second requirements. At block 450, thesystem may relax any one of the first, second, and/or third requirementswhile solving for a fourth of the objectives to generate a forthrequirement. At block 460, the system may take the solved-forrequirements from block 430, block 440, or block 450 to generate anallocation plan to control serving the ad impressions according to therange of possible proportions of allocations between the GD contractdemand and the NGD demand on the spot market. The proportions ofallocations may range anywhere from zero to 100%. The system may executethe method of FIG. 8 through an optimizer executing instructions storedin memory of a server. The system may also allow prioritization of theobjectives and therefore solve for the objectives to generate theallocation plan in order of the prioritization.

This disclosure encompasses methods, systems for practicing the methods,programmable data processing apparatus and/or program data carriers thatstore code enabling a general purpose computer to practice the subjectmatter when coupled in data communication with sources of advertiserinformation, sources of media distributor information, and advertisingcopy that can be inserted when opportunities are reported by the mediadistributors.

FIG. 9 illustrates a practical embodiment as a block level diagramwherein the ad distribution system is configured as a computer system750 that is coupled for data communications, for example to providemedia in the form of HTML web pages and graphics files over acommunication path traversing the Internet 755 to various remote users757, who may be appropriate targets for advertising content provided byadvertisers 200. The computer system 750 can be associated with aservice such as a directory service or search engine, or a retail orwholesale outlet or any of various operations whose activities includetransmission of media to users 757.

The system 750 as shown can include one or more processors 772,implemented using a general or special purpose processing engine such asa microprocessor, controller or other control logic configuration. Inthe example shown, processor 772 is coupled via a bus 780 to program anddata memory 774, an interface 776 for input/output with a localoperator, including, for example, a keyboard, mouse, display, etc., anda communications interface 778. The communications interface isgenerally shown coupled for communications with advertisers 200 or overthe Internet with remote users 757; however it is likewise possible thatother specific techniques could be employed to deliver data from theadvertiser to system 750, such as hand transferred data carriers,telephone discussions or even paper exchanges. The manner oftransmitting media to the users 757 likewise is not limited to web pagedata transmission and could comprise, for example, cable or other videoprogram distribution among other possible embodiments.

The memory 774 of the computing system advantageously includes randomaccess volatile memory and ROM, disc or flash nonvolatile memory forinitialization. The program instructions are stored in and executed fromthe program memory to carry out the functions discussed above. Thememory can include persistent data storage for accumulated datarespecting advertiser and user information, for example on hard drives.Advantageously, the memory 774 of system 750 can contain locally storedversions of advertising copy that is to be inserted, especially forservicing guaranteed demand. The memory 774 also can receive, preferablystore and insert at least some advertising copy from advertisers 22 whoundertake to use ad impressions obtained on the ad hoc spot market.

Alternatively or in addition, at least part of the advertising copy tobe inserted can be stored remotely and accessed by providing to thebrowser at the user system the appropriate URLs identifying advertisingcontent to be inserted. For example, the system 750 can store and submitto the user browser a network address for graphics or other content tobe inserted, which address refers to a system at or associated with theadvertiser 200, which system is coupled for web communications and isconfigured to respond to an IP request for addressed graphic or mediacontent. That content can be obtained by bidirectional IP communicationsbetween the browser and the system where the content is stored.

The persistent storage devices of memory 774 may include, for example, amedia drive and a storage interface for video or other substantialstorage capacity needs. The media drive can include a drive or othermechanism to support a storage media. For example, a hard disk drive, afloppy disk drive, a magnetic tape drive, an optical disk drive, a CD orDVD drive (R or RW), or other removable or fixed media drive may beemployed. The storage media can include, for example, a hard disk, afloppy disk, magnetic tape, optical disk, a CD or DVD, or other fixed orremovable medium that is read by and written to by the media drive.

The terms “computer program medium” and “computer useable medium” andthe like are used generally to refer to media such as, for example,memory 774, various storage devices, a hard disk and hard disk drive andthe like. These and other various forms of computer useable media may beinvolved in carrying one or more sequences of one or more instructionsto the processor 772 for execution. Such instructions, generallyreferred to as “computer program code” (which may be grouped in the formof computer programs or other groupings), when executed, enable thecomputing system 750 to perform features or functions of the embodimentsdiscussed herein.

Alternatively or in addition, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments may broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that may be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system may encompass software, firmware, and hardwareimplementations.

The methods described herein may be implemented by software programsexecutable by a computer system. Further, implementations may includedistributed processing, component/object distributed processing, andparallel processing. Alternatively or in addition, virtual computersystem processing maybe constructed to implement one or more of themethods or functionality as described herein.

The network could be the Worldwide Web and the advertising copy couldcomprise banner ads, graphics in fields of specific size and placement,overlaid moving pictures or animation, redirection to a different URL,etc. The same targeting abilities are also applicable to networks thatare interactive to a lesser degree, such as cable television adinsertion, which might be done at a head end or at a hub, or even from asubscriber-specific set top box.

Although components and functions are described that may be implementedin particular embodiments with reference to particular standards andprotocols, the components and functions are not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP)represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

The illustrations described herein are intended to provide a generalunderstanding of the structure of various embodiments. The illustrationsare not intended to serve as a complete description of all of theelements and features of apparatus, processors, and systems that utilizethe structures or methods described herein. Many other embodiments maybe apparent to those of skill in the art upon reviewing the disclosure.Other embodiments may be utilized and derived from the disclosure, suchthat structural and logical substitutions and changes may be madewithout departing from the scope of the disclosure.

While one may not conclude definitely that any given subject (user orbrowser) responds favorably if exposed to information or advertising,for example by purchasing an advertised product or service, one canestablish a set of variables to characterize members of a population, todetermine values for those variables that are most characteristic ofactual purchasers (and by implication to assess the quality of adtargets). The statistical methods above may enable correlation of a setof variable values with selected subsets of the population consistentwith purchasers. Statistical methods also enable correlation among thevariables themselves. The result is a set of criteria such as age,gender, location, income range, education, family status, etc., andvarious rules of thumb that attempt to use combinations of certainvalues of these criteria to make conclusions about the characteristicsand buying preferences of customers.

Variably defined subsets of the population are thereby rated for thelikelihood that members of each subset becomes a purchaser if exposed toadvertising. The subsets of the population can be distinguished by theextent to which members are correlated to an ideal target for anadvertising piece.

There are mathematical ways to correlate variable values that may beknown about the population of subjects with other variable values thatmay not be known. There are also ways to infer information such asdescriptive and demographic details about subjects, based on thesubject's current activities, including the websites that a subject maybe visiting, the entertainment programs being viewed, the periodicalpublications that the person reads, etc. If an advertiser is promoting aproduct that is associated with the content of a website or apublication, then advertising on the website or in the publication maybe more valuable to the advertiser than advertising elsewhere orrandomly, because the subjects who are exposed to the advertising arerelatively more highly correlated with likely purchasers than othersubjects and are more likely to actually see the advertising.

An advertiser typically does not have close access to an isolatedpopulation of subjects who are all very highly correlated with an ideallikely purchaser. Even if the advertiser had access to such apopulation, the advertiser may not devote 100% of its advertising effortto that population. The advertiser also may want to devote advertisingefforts to other populations that are perhaps not so highly correlated,but where advertising still has a positive effect. For example, anadvertiser may seek to spread advertising expenditures over a wide rangeof subjects and over a wide geographic area, while perhaps biasing itsefforts toward subjects who are or might be correlated with ahypothetical ideal purchaser.

The advertiser may determine a profile of representative advertisingover which advertising expenditures shall be devoted. This profile maybe discussed with possible advertising outlets such as advertisingbrokers, advertising services (including on-line services such as thatoffered by Yahoo!), media outlets such as web page operators and cablemedia distributors, print publishers and others similarly situated.Negotiations may ensue on the basis that the party controlling the adimpressions demands payment and competing advertisers who want to usethe ad impressions are willing to pay for the ad impressions in amountsthat related to the extent to which the ad impressions match theadvertisers' representative profiles of what the advertisers demand.Matching the use of impressions to adhere to the representative aspectssought by the advertiser may be an objective. Maintaining“representativeness” may achieve long term value.

The market for advertising on Internet web pages is particularly welldeveloped because information is available to characterize the web pageusers (the potentially targeted subjects). Infrastructure is in placefor changeably inserting ad graphics and moving pictures, such asInternet browsers. Data from click streams and sometimes from locallystored cookies can carry context and history information forward in timeas the user surfs through different pages. Internet service providersmake at least generalized information on subscribers availableroutinely, such as the subscriber's zip code. These information sourcesenable information to be collected to gauge the characteristics of usersand enable an advertiser to define a representative advertisingallocation for which the advertiser contracts.

Internet web page operators are also in a good situation for collectingdata about information distribution events, such as reporting on theavailability and use of ad impressions. Executed ad impressions can becounted and reported with associated context information, time of day,location of recipient and so forth. This information enables theoperators to forecast the number of impressions and the characteristicsof users that are likely to be available to receive impressions ready tobe allocated to those users at a future date and time. The informationallows up to the moment monitoring of use of the ad impressions forreporting compliance with contractual obligations to distribute a givennumber of ads of a given type in a given time window.

Advertisers contract with advertising distributors and advertisingservices to make use of ad impressions that are available to thedistributor or service. The advertising distributor might be a websiteoperator or an advertising warehouse that in turn contracts with websiteoperators. Available impressions may be determined in number and withrespect to attributes that determine the value of the impressions to theadvertiser. The attributes include characteristics that enable theadvertiser to judge how representative the recipients of the impressionswill be, compared to likely purchasers and to the advertiser's desiredprofile of ad distribution. The advertising distributor may agree todistinguish among potential users to whom impressions are delivered, forexample by the attributes of the users or the web content that the usersview. This aspect may be written into the contract. The advertisingdistributor may commit to delivering a given number of impressions tousers of defined characteristics or in a defined context over a giventime window at some point in the future.

The advertiser may contract with the advertising distributor to delivera stated number of ad impressions to a stated number of website viewershaving stated demographic or other properties that correspond with therepresentativeness aspects dictated by an advertiser. There may bealternative ways in which the website operator could meet itsobligations. As one example, if the agreement is to deliver impressionsto users in a certain age group, the website operator might devote alarge ratio of available impression opportunities at a time of day whenthe on-line user population of the age group is low, or a smaller ratioof available impression opportunities at a time of day when thepercentage of users in that age group is higher, and in either case getthe number of impressions needed to meet the contractual obligation.

The website operator or other advertising distributor has degrees offreedom in which to operate but may need information to define thevariations in users by factors that matter, such as the correlation ofuser age to time of day of on-line access, in the example of a timediscrimination aspect. There are various such correlations possiblebetween category ratings that are known or might be inferred.

In order to assess its ability to meet contractual obligations, theadvertising distributor projects an estimate of the number of users ofgiven characteristics at some future date and time when offering to sellad impressions to an advertising campaign manager negotiating for theadvertiser. If the seller of ad impressions (the advertisingdistributor) guarantees that a certain number of ad impressions areexecuted to users of given attributes, the seller may be bound tocomply, subject to possible contractual penalties.

A seller may decide to guarantee a number of available impressions thatare relatively sure to be available at the future data and time. Then ifan excess number of impressions actually become available for executionat that time, the seller may seek to exploit them in sales under shortterm contracts, in an ad hoc spot market or by auction that could occurat any time up to the moment that an ad impression is used. Theimpressions that were committed by contract according to prudentprojections made ahead of time can be deemed “guaranteed” impressions.The remaining impressions are “excess” or “non-guaranteed” impressionsand may be sold on last minute terms or on “best efforts” commitments bythe ad distributor.

In existing markets for on-line advertising, the manner of sale and theuse of guaranteed and non-guaranteed ad impressions may be distinctlydifferent for the two types. Based on their confidence in projections ofad availability, the seller of ad impressions may prefer to sellguaranteed impressions and to develop long term relationships withadvertisers characterized by dependability in meeting obligations.However, undue caution when making projections may leave saleable adsunsold, or may affect the prices that quality ad impressions maycommand. Furthermore, the ability to correlate user characteristics withad impressions accurately may be best immediately before the ads areused. Therefore, some of the highest quality ads (namely those that arehighly correlated with some desired target category) arise only after itis too late to handle them in guaranteed contracts. For theseimpressions, a second marketplace is advantageous, apart from themarketplace in the sale of projected future impressions under contractsthat contain obligations as to the number of impressions that provided.This second marketplace is not based substantially on promises of futureperformance and instead is based on exploiting currently availableopportunities.

If the advertising distributor was cautious when negotiating contractsto sell guaranteed impressions, the advertising distributor may havereserved a substantial portion of the impressions that were projected tobecome available, to avoid contractual penalties if the projectionsprove too optimistic. These may be sold or else wasted.

If impressions become available that are matched to an advertiser'srepresentative profile, the impressions have a high value in advertisingeffectiveness to that advertiser. These non-guaranteed impressions mightbe sold at a high price. Assuming that some proportion of projectedimpressions are to be reserved to ensure the ability to meetobligations, a problem is presented in how optimally to allocate theimpressions between the guaranteed and non-guaranteed categories whenplanning and negotiating contracts for use of projected future adimpressions. Assuming that the decisions have been made, the situationmay change when projections are proved or disproved in reality. Theabove system and method, including optimizer 310, may optimally allocateemergent supply of ad impressions either to obligated/guaranteedimpressions or to non-guaranteed impressions, in a manner that is agileand quick.

The system and method may consider multiple objectives. The advertisingdistributor meets his contractual obligations, and delivers qualityimpressions to the advertiser in exchange for value received. Theadvertising distributor's long term performance under these objectives,including meeting contractual obligations for delivery of guaranteedimpressions, is important to maintaining mutually beneficial relationsbetween the advertising distributor and its customers, namely theadvertisers.

The advertising distributor may maximize revenues obtained in exchangefor use of the ad impressions that are available. Revenues can bemaximized when accurate projections can be made, including forecastingthe supply of impressions that are available, assessing the demand forguaranteed impressions and forecasting the future demand in the event ofshort notice ad hoc sales of excess impressions, by auction orotherwise.

The foregoing situation can be considered a confluence of overlappingmarketplaces. For each marketplace, the impressions (informationexposures) that are available according to projections, or theimpressions that actually prove to be available when the time arrives,each represent a finite supply of information distributionopportunities. These information distribution opportunities need to beallocated to the demand for use of ad impression opportunitiesassociated with highly representative advertiser-targeted groups. Theallocation may maximize representativeness of ad impressions compared tothe advertiser's targeting, which comes from ensuring that guaranteedimpressions are faithfully delivered. The allocation may maximize therevenue to the advertising distributor, who may be a media operator, byensuring that no impressions go unsold, or are sold at prices that areless than the ad impressions should reasonably command.

A given number of impressions is projected to be available. If theadvertising distributor decides to use some number of the projectedimpressions under guaranteed contracts, then the number available for adhoc auction is reduced, and vice versa. The above system and method mayprovide an optimal and efficient technique to control the relativeallocations of guaranteed ad impressions under contracts versusnon-guaranteed ad impressions to be sold on the spot market.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe true spirit and scope of the description. Thus, to the maximumextent allowed by law, the scope is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

We claim:
 1. An advertisement impression distribution system,comprising: a data processing system including a processor and memory,the data processing system programmed to generate, as goal programming,an allocation plan for serving a number of advertisement impressionseligible to meet a demand of a plurality of advertisers, the allocationplan to allocate a first portion of advertisement impressions to satisfyguaranteed demand arising from pre-arranged contracts and a secondportion of advertisement impressions to satisfy non-guaranteed demandauctioned in real time in an advertisement spot market, as theadvertisement impressions become available, where the guaranteed andnon-guaranteed demands comprise competing demands for advertisementimpressions having overlapping targeting attributes that target featuresmatching advertiser targeting profiles in a high-dimensional targetingspace, and the number of advertisement impressions are changeable as aresult of one or more events; where the data processing system includesan optimizer, the optimizer programmed to: establish a relationshipbetween the first portion of advertisement impressions and the secondportion of advertisement impressions, the relationship defining a rangeof possible proportions of allocation of the first portion ofadvertisement impressions and the second portion of advertisementimpressions; and impose multiple objectives on the relationship,comprising: maximizing guaranteed demand representativeness throughproportionate allocation of advertisement impressions to advertiserswith advertisements containing attributes matching the impressions; andmoderating an increase in the number of advertisement impressionsavailable for allocation to the first and second portions, to minimize acost associated with reducing a quality of the advertisement impressionsas a volume of the advertisement impressions increases; solve for afirst of the objectives, resulting in a first requirement, then solvefor a second of the objectives to generate a second requirement whilerelaxing the first requirement, where relaxing is to allow departurefrom a determined optimum value; where the data processing system isfurther programmed to output the allocation plan to an ad serving moduleof the data processing system to control serving of the advertisementimpressions according to the range of possible proportions of allocationbetween the first and the second portions.
 2. The system of claim 1,where the one or more events that cause the advertisement impressions tochange comprise one or more of: changing a score threshold forqualifying a user into a specified interest category; changingnavigational links on a web page; and dynamically changing displayedcontent on a web page; where the interest category depends on userbehavior, and where the cost of the advertisement impressions comprisesa cost associated with a function that increases with an increase in thenumber of advertisement impressions.
 3. The system of claim 1, where themultiple objectives further comprise: maximizing non-guaranteed demandrevenue from non-guaranteed contracts of advertisers, and minimizingunder-delivery penalties that result from not fulfilling guaranteedcontract delivery requirements.
 4. The method of claim 3, where thefirst requirement comprises a minimum penalty cost and the secondrequirement comprises a maximum non-guaranteed demand revenue, and whererelaxing the first requirement comprises allowing the first requirementto be greater than the solved minimum penalty cost.
 5. The method ofclaim 3, where the optimizer is further programmed to: solve a thirdobjective to generate a third requirement while relaxing one or more ofthe first and second requirements.
 6. The system of claim 5, where thefirst requirement comprises a maximum non-guaranteed demand revenue, thesecond requirement comprises a minimum penalty cost, and the thirdrequirement comprises a maximum guaranteed demand representativeness,and where relaxing the one or more of the first and second requirementscomprises one or more of: allowing the first requirement to be less thanthe solved maximum non-guaranteed demand revenue; and allowing thesecond requirement to be greater than the solved minimum penalty cost.7. The system of claim 5, further comprising: solving a fourth objectiveto generate a fourth requirement while relaxing one or more of thefirst, second, and third requirements.
 8. The system of claim 7, wherethe first, second, and third requirements comprise a minimum penaltycost, a maximum non-guaranteed demand revenue, and a minimum supply costof the advertising impressions in any order, and where the fourthrequirement comprises a maximum guaranteed demand representativeness. 9.The system of claim 8, where the first through fourth objectives areordered according to priority and the highest priority objective issolved first.
 10. The system of claim 3, where the optimizer is furtherprogrammed to: combine the under-delivery penalties, the non-guaranteeddemand revenue, and the supply cost of the advertisement impressionsusing a weighted sum of monetary objectives, the method furtherincluding: first optimizing the monetary objectives; and next optimizingthe guaranteed demand representativeness.
 11. A method for distributingadvertisement impressions, the method executable by a data processingsystem having a processor and memory, and in the memory storedinstructions, comprising: generating, with the system through executionof the instructions and as goal programming, an allocation plan forserving a number of advertisement impressions eligible to meet a demandof a plurality of advertisers, the allocation plan to allocate a firstportion of advertisement impressions to satisfy guaranteed demandarising from pre-arranged contracts and a second portion ofadvertisement impressions to satisfy non-guaranteed demand auctioned inreal time in an advertisement spot market, as the advertisementimpressions become available, where the guaranteed and non-guaranteeddemands comprise competing demands for advertisement impressions havingoverlapping targeting attributes that target features matchingadvertiser targeting profiles in a high-dimensional targeting space, andthe number of advertisement impressions being changeable as a result ofone or more events, where generating comprises: establishing arelationship between the first portion of advertisement impressions andthe second portion of advertisement impressions, the relationshipdefining a range of possible proportions of allocation of the firstportion of advertisement impressions and the second portion ofadvertisement impressions; and imposing multiple objectives on therelationship, comprising: maximizing guaranteed demandrepresentativeness through proportionate allocation of advertisementimpressions to advertisers with advertisements containing attributesmatching the impressions; and moderating an increase in the number ofadvertisement impressions available for allocation to the first andsecond portions, to minimize a cost associated with reducing a qualityof the advertisement impressions as a volume of the advertisementimpressions increases; solving for a first of the objectives, resultingin a first requirement, followed by solving for a second of theobjectives to generate a second requirement while relaxing the firstrequirement, where relaxing is to allow departure from a determinedoptimum value; and outputting the allocation plan to an ad servingmodule of the system through execution of the instructions, to controlserving of the advertisement impressions according to the range ofpossible proportions of allocation between the first and the secondportions.
 12. The method of claim 11, where the one or more events thatcause the advertisement impressions to change comprise one or more of:changing a score threshold for qualifying a user into a specifiedinterest category; changing navigational links on a web page; anddynamically changing displayed content on a web page; where the interestcategory depends on user behavior, and where the cost of theadvertisement impressions comprises a cost associated with a functionthat increases with an increase in the number of advertisementimpressions.
 13. The method of claim 11, where the multiple objectivesfurther comprise: maximizing non-guaranteed demand revenue fromnon-guaranteed contracts of advertisers, and minimizing under-deliverypenalties that result from not fulfilling guaranteed contract deliveryrequirements.
 14. The method of claim 13, where the first requirementcomprises a minimum penalty cost and the second requirement comprises amaximum non-guaranteed demand revenue, and where relaxing the firstrequirement comprises allowing the first requirement to be greater thanthe solved minimum penalty cost.
 15. The method of claim 13, furthercomprising: solving a third objective to generate a third requirementwhile relaxing one or more of the first and second requirements.
 16. Themethod of claim 15, where the first requirement comprises a maximumnon-guaranteed demand revenue, the second requirement comprises aminimum penalty cost, and the third requirement comprises a maximumguaranteed demand representativeness, and where relaxing the one or moreof the first and second requirements comprises one or more of: allowingthe first requirement to be less than the solved maximum non-guaranteeddemand revenue; and allowing the second requirement to be greater thanthe solved minimum penalty cost.
 17. The method of claim 15, furthercomprising: solving a fourth objective to generate a fourth requirementwhile relaxing one or more of the first, second, and third requirements.18. The method of claim 17, where the first, second, and thirdrequirements comprise a minimum penalty cost, a maximum non-guaranteeddemand revenue, and a minimum supply cost of the advertising impressionsin any order, and where the fourth requirement comprises a maximumguaranteed demand representativeness.
 19. The method of claim 17, wherethe first through fourth objectives are ordered according to priorityand the highest priority objective is solved first.
 20. The method ofclaim 13, further comprising: combining the under-delivery penalties,the non-guaranteed demand revenue, and the supply cost of theadvertisement impressions using a weighted sum of monetary objectives,the method further including: first optimizing the monetary objectives;and next optimizing the guaranteed demand representativeness.